Today, more data is stored than ever before. As the cost of storing data decreases, the amount of data stored increases. For companies it is estimated that digital data storage will increase by 40% year over year, representing a ten-fold increase in storage capacity over a 7-year period.
As data storage increases, however, so does the need to manage and process the data. Data management systems have not improved in line with increased demand for data storage. As a result, it has become more difficult to organize, explore, and view the data being stored. One method of data management includes management and analysis of metadata—a set of data that describes and gives information about other data. Companies using this method generally hire data analysts and information technology scientists to make sense of metadata. In addition to personnel costs, this can create additional issues for the companies managing their data. In particular, data analysts must translate information from managed data into insights that can be understood by executives or high-level decision-makers. In some cases, this can limit the ability of high-level decision-makers to ask relevant and insightful questions. In other cases, this can create a bottleneck or latency in data-driven decision-making, preventing decision-makers from acting quickly in response to new insights.
Thus, a heretofore unaddressed need exists in the industry to address the aforementioned deficiencies and inadequacies.